LoRA Code Breakdown and Explanation | Generated by AI

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I’ll break down the LoRA demo code into key pieces, explaining each part in detail before moving to the next. This will help illustrate how LoRA works in a clear, step-by-step manner. The code is based on the previous example, using PyTorch to implement LoRA for a simple linear layer.

Code Piece 1: LoRA Layer Definition

import torch
import torch.nn as nn

class LoRALayer(nn.Module):
    def __init__(self, in_features, out_features, rank=4):
        super(LoRALayer, self).__init__()
        # Original frozen weights
        self.linear = nn.Linear(in_features, out_features)
        self.linear.weight.requires_grad = False  # Freeze original weights
        # LoRA parameters: low-rank matrices A and B
        self.lora_A = nn.Parameter(torch.randn(in_features, rank))
        self.lora_B = nn.Parameter(torch.randn(rank, out_features))
        self.scaling = 1.0  # Scaling factor for LoRA updates

Explanation

This piece defines the LoRALayer class, which implements the LoRA technique. Here’s what’s happening:

This setup ensures that only the small lora_A and lora_B matrices are updated during training, drastically reducing the number of trainable parameters.

Code Piece 2: LoRA Forward Pass

    def forward(self, x):
        # Original linear transformation + LoRA adjustment
        original = self.linear(x)
        lora_adjustment = self.scaling * torch.matmul(torch.matmul(x, self.lora_A), self.lora_B)
        return original + lora_adjustment

Explanation

This piece defines the forward pass of the LoRALayer, which computes the output of the layer:

The low-rank structure (rank is small, e.g., 4) ensures the adjustment is computationally cheap and parameter-efficient compared to updating the full weight matrix.

Code Piece 3: Toy Dataset and Training

def create_toy_dataset(n_samples=1000):
    X = torch.randn(n_samples, 64)  # Random input features
    y = torch.randn(n_samples, 10)  # Random target outputs
    return X, y

def train_model(model, X, y, epochs=10, lr=0.01):
    criterion = nn.MSELoss()
    optimizer = optim.Adam([param for param in model.parameters() if param.requires_grad], lr=lr)
    
    for epoch in range(epochs):
        model.train()
        optimizer.zero_grad()
        outputs = model(X)
        loss = criterion(outputs, y)
        loss.backward()
        optimizer.step()
        print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item():.4f}")

Explanation

This piece creates a toy dataset and trains the LoRA-adapted model:

This setup demonstrates how LoRA fine-tunes only the low-rank matrices, keeping the process lightweight.

Code Piece 4: Main Execution and Parameter Count

def main():
    # Set random seed for reproducibility
    torch.manual_seed(42)
    
    # Create toy dataset
    X, y = create_toy_dataset()
    
    # Initialize model with LoRA
    model = LoRALayer(in_features=64, out_features=10, rank=4)
    
    # Count trainable parameters
    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    total_params = sum(p.numel() for p in model.parameters())
    print(f"Trainable parameters: {trainable_params}")
    print(f"Total parameters: {total_params}")
    
    # Train the model
    train_model(model, X, y)

if __name__ == "__main__":
    main()

Explanation

This piece ties everything together and highlights LoRA’s efficiency:

When you run this, you’ll see the number of trainable parameters is much smaller than the total, and the model trains quickly due to the low-rank updates.

Running the Code

To run this, you need PyTorch installed (pip install torch). Save the full code as a .py file and execute it. The output will show the number of parameters and the training loss over 10 epochs, demonstrating LoRA’s efficiency in fine-tuning.

LoRA: Low-Rank Adaptation of Large Language Models

Let me know if you’d like to dive deeper into any part or modify the example!

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